{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "raised-fellowship",
   "metadata": {},
   "source": [
    "# figs_twitter.ipynb\n",
    "\n",
    "Author: MB\n",
    "\n",
    "Date run: April 1, 2021\n",
    "\n",
    "Location: HPC\n",
    "\n",
    "The purpose of this script is to read in a dataset of retweets of Trump tweets and generate three figures\n",
    "* the cumulative number of retweets over time\n",
    "* average number of followers per retweet by engagement type\n",
    "* average number of engagements per retweet by engagement type\n",
    "\n",
    "Data in: \n",
    "* Decahose (filtered to just retweets of Trump tweets) -- 10% of all tweets\n",
    "* Dataset of interventions to tweets\n",
    "* labels for Trump tweets\n",
    "\n",
    "Data out: the aforementioned figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sophisticated-trouble",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import datetime\n",
    "import glob\n",
    "import json\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "concrete-artist",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_frame(frame, tweet_id):\n",
    "    '''\n",
    "    this function forward fills metrics to fill in missing minutes where there is not a record of the number of retweets\n",
    "    it then returns the filled dataframe\n",
    "    \n",
    "    :pd.DataFrame frame: a dataframe of tweets with the number of seconds since posting `seconds` and number of retweets at that timestamp `count`\n",
    "    '''\n",
    "    # sort dataframe in ascending order by seconds\n",
    "    frame = frame.sort_values('seconds')\n",
    "    \n",
    "    # append the missing seconds to the dataframe with an empty count value\n",
    "    seconds_we_have = frame['seconds'].unique()\n",
    "    frame = frame.append([{'seconds':i} for i in range(0, 86401) if i not in seconds_we_have]).sort_values('seconds')\n",
    "    \n",
    "    # forward fill the dataframe (carry values up to the next non-null value)\n",
    "    frame['og_retweet_count'] = frame['og_retweet_count'].fillna(method='ffill')\n",
    "    frame['tweet_id'] = tweet_id\n",
    "    \n",
    "    return frame\n",
    "\n",
    "def convert_datetime(time):\n",
    "    '''\n",
    "    converts twitter timestamp string into a datetime object\n",
    "    \n",
    "    :str time: the twitter-style timestamp for tweet creation\n",
    "    :returns date: a datetime.datetime object corresponding to the time input\n",
    "    '''\n",
    "    if isinstance(time, str):\n",
    "        return datetime.datetime.strptime(time, '%a %b %d %H:%M:%S +0000 %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "meaning-floating",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set colors\n",
    "no_intervention_color = '#16697a'\n",
    "soft_intervention_color = '#ffa62b'\n",
    "hard_intervention_color = '#721121'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "reduced-colon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the intervention dataset\n",
    "interventions = pd.read_json('../data/interventions.json', orient='records', lines=True, dtype={'id_str':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "retained-habitat",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the labels and unique trump tweets \n",
    "labels = pd.read_csv('../data/labels.csv')\n",
    "trump_tweets = pd.read_csv('../data/trump_tweets.csv', dtype={'retweeted__id':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "silver-carroll",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy `Final` data to `political` because I like that better\n",
    "trump_tweets['political'] = labels['Final']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "furnished-membrane",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter Trump tweets only to tweets labelled as political\n",
    "political_tweets = trump_tweets[trump_tweets['political'] == 'political']['retweeted__id'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "appropriate-video",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter interventions to Trump tweets that are political\n",
    "interventions = interventions[interventions['id_str'].isin(political_tweets)].drop_duplicates('id_str').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "therapeutic-cornell",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get tweet IDs by intervention type\n",
    "no_intervention = interventions[(interventions['hard_intervention'] == False) & (interventions['soft_intervention'] == False)]['id_str'].tolist()\n",
    "soft_intervention = interventions[(interventions['hard_intervention'] == False) & (interventions['soft_intervention'] == True)]['id_str'].tolist()\n",
    "hard_intervention = interventions[(interventions['hard_intervention'] == True)]['id_str'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satisfied-terrorism",
   "metadata": {},
   "source": [
    "# Retweet Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dietary-hollywood",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load retweet data\n",
    "retweets = pd.read_json('../data/twitter_posts.json.bz2', orient='records', lines=True, compression='bz2',\n",
    "                        dtype={'tweet_id':str, 'og_tweet_id':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "brazilian-calculator",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter by tweets that happened within 24 hours of the tweet being published\n",
    "retweets = retweets[(retweets['created_at'] - retweets['og_created_at'] > datetime.timedelta(minutes=0))\n",
    "                    & (retweets['created_at'] - retweets['og_created_at'] <= datetime.timedelta(hours=24))\n",
    "             ].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "comparable-participant",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate time diff between retweet and og tweet\n",
    "retweets['time_diff'] = (retweets['created_at'] - retweets['og_created_at']).values / np.timedelta64(1, 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "prospective-training",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate hours, minutes and seconds\n",
    "retweets['seconds'] = retweets['time_diff']\n",
    "retweets['minutes'] = retweets['seconds'] / 60\n",
    "retweets['hours'] = retweets['minutes'] / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "proved-noise",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter to political tweets \n",
    "retweets = retweets[retweets['og_tweet_id'].isin(political_tweets)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "psychological-contributor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# forward fill dataframes for missing seconds \n",
    "filled_frames = pd.concat([fill_frame(frame, _) for _, frame in retweets.groupby('og_tweet_id')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "placed-signature",
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by intervention type\n",
    "hard = filled_frames[filled_frames['tweet_id'].isin(hard_intervention)].copy()\n",
    "soft = filled_frames[filled_frames['tweet_id'].isin(soft_intervention)].copy()\n",
    "none = filled_frames[filled_frames['tweet_id'].isin(no_intervention)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "representative-necklace",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate cumulative rolling average for each intervention type\n",
    "hard_plot = hard.groupby('seconds')['og_retweet_count'].mean().rolling(window=3600, min_periods=20).mean()\n",
    "soft_plot = soft.groupby('seconds')['og_retweet_count'].mean().rolling(window=3600, min_periods=20).mean()\n",
    "none_plot = none.groupby('seconds')['og_retweet_count'].mean().rolling(window=3600, min_periods=20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "rational-extreme",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot cumulative retweet rolling averages\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(hard_plot, label='Hard Intervention', color=hard_intervention_color, linewidth=4)\n",
    "plt.plot(soft_plot, label='Soft Intervention', color=soft_intervention_color, linewidth=4)\n",
    "plt.plot(none_plot, label='No Intervention', color=no_intervention_color, linewidth=4)\n",
    "\n",
    "plt.legend(fontsize=14)\n",
    "locs = [i*3600 for i in range(25)]\n",
    "plt.xticks(locs, range(25), rotation=45, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14)\n",
    "plt.xlabel('Time since tweet publication (hours)', fontsize=14)\n",
    "plt.ylabel('Number of Retweets', fontsize=14)\n",
    "plt.title('Growth of Twitter Posts by Intervention', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/twitter_spread_of_tweets.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "smart-berry",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split tweets into different intervention types\n",
    "hard_ct = retweets[retweets['og_tweet_id'].isin(hard_intervention)].copy()\n",
    "soft_ct = retweets[retweets['og_tweet_id'].isin(soft_intervention)].copy()\n",
    "none_ct = retweets[retweets['og_tweet_id'].isin(no_intervention)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "approximate-speed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot average number of followers by intervention type\n",
    "plt.figure(figsize=(8, 10))\n",
    "\n",
    "plt.bar(0, width=.5, height=hard_ct.groupby('tweet_id')['user__followers_count'].sum().mean(), color=hard_intervention_color, label='Hard Intervention')\n",
    "plt.bar(1, width=.5, height=soft_ct.groupby('tweet_id')['user__followers_count'].sum().mean(), color=soft_intervention_color, label='Soft Itervention')\n",
    "plt.bar(2, width=.5, height=none_ct.groupby('tweet_id')['user__followers_count'].sum().mean(), color=no_intervention_color, label='No Intervention')\n",
    "plt.legend(fontsize=14)\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14, bbox_to_anchor = [1, .5])\n",
    "plt.xlabel('Intervention Type', fontsize=14)\n",
    "plt.ylabel('Average Total Followers', fontsize=14)\n",
    "plt.title('Average Total Exposure per Tweet by Intervention Type - Twitter', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/twitter_avg_exposure.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bearing-cooling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the average number of favorites by intervention type\n",
    "hard_interactions = hard_ct.groupby('og_tweet_id')['og_favorite_count'].max().mean()\n",
    "soft_interactions = soft_ct.groupby('og_tweet_id')['og_favorite_count'].max().mean()\n",
    "none_interactions = none_ct.groupby('og_tweet_id')['og_favorite_count'].max().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "analyzed-machine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KS1tubEXrRHYX/dpkN/j9iyzgL48K4Adk0z4akL0lv2dKaXVctuobZG/rv0U2H1grh6lk9y0c83XecZGk+s5pG5IkSVJB3jAoSZIkFWR4liRJkgpyznMt+OY3v5keeKCmPpRKkqSVQlRfRVr5OfJcCz76yHtlJEmSVkWGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGN6roDklYit/Wt6x6s/Ia+Wtc9kCR9DY48S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBKzQ8R8QvI+L5iJgVEdMi4t6I6FtWJyLirIiYGBHzIuLxiNikrE7biLg5Imbm280RsWZZnU0j4om8jQ8j4v8iIsrqDImIsRGxIP+63/L2RZIkSauPFT3yvBNwNbAtsAuwCHgkItqV1DkVOAk4HtgSmAo8HBFrlNS5FRgAfDPfBgA3VxZGRGvgYWBK3sYJwCnAiSV1BgHDgb8Cm+df74iIrZezL5IkSVpNREqp7i4e0QqYCXwnpXRvPjI8EfhDSum8vE5zstB6ckrp2ojoA4wFtksp/Tevsx3wFNA7pTQuIn4E/B7olFKal9f5FfAjYJ2UUoqI4UC7lNLgkv48AkxLKQ0t0pelPa+BAwemUaNG1dwLJdUXt/Wtvo6Wbeirdd0DqbZE9VWklV9dz3leI+/DjHx/PaAz8FBlhTz8Pkk2Wg0wCJgNPF3Szn+BOWV1nqoMzrkHgS5Aj5I6D7GkB0vaKNIXSZIkrUbqOjxfDrwEPJPvd86/TimrN6WkrDPZ6PDnQ+b546lldapqgwJ1OpfVW1adz0XE0RExKiJGTZs2rbxYkiRJq4A6C88RcQmwHTAkpbS4rvpRU1JK16WUBqaUBnbo0KGuuyNJkqRaUCfhOSIuBYYCu6SU3i0pmpx/7VR2SqeSsslAh9KVM/LHHcvqVNUGBepMLqu3rDqSJElajazw8BwRl/NFcH6jrHg8WTAtvYmvGbA9X8xxfgZoRTZnudIgoGVZne3zcysNJrsB8L2SOoNZ0uCSNor0RZIkSauRFb3O81XA4cDBwIyI6JxvreDzucuXAadFxP75GtDDyG4QvDWv8zrwAHBtRAzKl5y7FhiRUhqXX+pWYC4wLCL6RsT+wC+AS0rmSl8O7BIRv4iI3hHxS2Dn/PqF+iJJkqTVS6MVfL0f51//XXb8bOCs/PEFQHPgKqAt8Bywe0rp05L6BwNXkq2OAfBP4LjKwpTSzIgYnLcximw1j4uBS0rqPB0R3wXOBc4B3gEOSik9V3KdIn2RJEnSaqJO13leVbnOs1ZZrvP89bnOs1ZdrvOs1UJdL1UnSZIkrTQMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQV1KiuOyBJ0vLo9OMz6roLK70pV59X112QVlqOPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQSs0PEfEDhHxz4j4MCJSRBxWVp6Wsl1VUmdYFeXPlrXTNCKujIiPImJOfs11yup0i4h78/KPIuKKiGhSVmfHiBgdEfMj4t2IOLYWXhZJkiStJFb0yHMr4FXgBGBeFeVrl2375sf/VlbvkbJ6e5WVXwYMAYYC2wOtgRER0RAg//ovYI28fChwAHBxZQMRsR5wH/A00B84H7gyIoYs31OWJEnSqqLRirxYSuk+skBKRAyronxy6X5EfBt4M6X0RFnVBeV1S85pA/wQODyl9HB+7PvABGA34EFgd2AToHtK6YO8zqnAnyLijJTSLOBYYGJK6fi86dcjYmvgZODvy/vcJUmStPKrt3OeI6IV8F3g+iqKt4uIqRHxZkRcHxEdS8q2ABoDD1UeyAPy68C2+aFBwOuVwTn3INA0P7+yzkMs6UFgYEQ0/opPS5IkSSuxehuegYOBJsBfyo4/APwA2BU4CdgKeDQimublnYHFwEdl503JyyrrTCkr/yg/b1l1ppCN1q+1nM9FkiRJq4AVOm1jOR0F3JNSmlZ6MKV0e8nuKxExmmxKxt7AXSuwf0uIiKOBowG6detWV92QJElSLaqXI88RsTkwkKqnbCwhpTQR+B+wYX5oMtCQL48Od8rLKut0KitfKz9vWXU6AYv48qg2KaXrUkoDU0oDO3ToUF23JUmStBKql+GZbAR3PNmqGssUEWsBXYFJ+aHRwEJgcEmddYA+ZCtnADwD9Clbvm4wsCA/v7LOYJY0GBiVUlq4PE9GkiRJq4YVvc5zq4jYPB9ZbgB0y/e7ldRpARwC3JBSSlWcf1FEDIqIHhGxE3AvMBW4GyClNBO4AbggInaLiP7AzcDLfBHGHwJeA26KiP4RsRtwIXB9vtIGwB+BrhFxWUT0iYgjgcOAi2r4ZZEkSdJKYkWPPA8EXsy35sDZ+eNzSuocBLQE/lzF+YuBTYF7gDfJbiYcBwxKKX1aUu9nZGF6OPBfYDawb0ppMUD+dW9gbl4+nGz5uZMrG0gpjSdbP3oH4CXgDOCnKSWXqZMkSVpNreh1nh8Hopo6f6bq4ExKaR6wR4HrLACOz7el1Xkf2Keadp4ABlR3PUmSJK0e6uucZ0mSJKneMTxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIJWaHiOiB0i4p8R8WFEpIg4rKx8WH68dHu2rE7TiLgyIj6KiDl5e+uU1ekWEffm5R9FxBUR0aSszo4RMToi5kfEuxFxbBX9/XFEjM/rjI6I7Wvw5ZAkSdJKZkWPPLcCXgVOAOYtpc4jwNol215l5ZcBQ4ChwPZAa2BERDQEyL/+C1gjLx8KHABcXNlARKwH3Ac8DfQHzgeujIghJXUOAi4HfpvXeRq4PyK6faVnLkmSpJVeoxV5sZTSfWShlYgYtpRqC1JKk6sqiIg2wA+Bw1NKD+fHvg9MAHYDHgR2BzYBuqeUPsjrnAr8KSLOSCnNAo4FJqaUjs+bfj0itgZOBv6eHzsRGJZSuj7fPz4ivgn8CPjlV3n+kiRJWrnVxznP20XE1Ih4MyKuj4iOJWVbAI2BhyoP5AH5dWDb/NAg4PXK4Jx7EGian19Z5yGW9CAwMCIa51M8tqiizkMl15EkSdJqpr6F5weAHwC7AicBWwGPRkTTvLwzsBj4qOy8KXlZZZ0pZeUf5ectq84UspH4tfKt4VLqdEaSJEmrpRU6baM6KaXbS3ZfiYjRZFMy9gbuqpteFRMRRwNHA3Tr5rRoSZKkVVF9G3leQkppIvA/YMP80GSyEeG1yqp2yssq63QqK68cSV5WnU7AIrJR6sqR6qrqVDkfO6V0XUppYEppYIcOHZb9xCRJkrRSqtfhOSLWAroCk/JDo4GFwOCSOusAfchWwwB4BuhTtnzdYGBBfn5lncEsaTAwKqW0MKX0WV63qjpPI0mSpNXSCp22ERGtgA3y3QZAt4jYHJieb2eRrXYxCehBtoTcVOBugJTSzIi4AbggIqYCHwOXAC+TLXEH2U19rwE3RcRJQHvgQuD6fKUNgD8Cx0XEZcC1wDeAw8iWtat0CXBzRIwE/ku2QkeX/FxJkiSthlb0nOeBwGMl+2fn21/IloDblOyGwTXJAvRjwIEppU9LzvkZ2fSK4UBz4N/AD1JKiwFSSosjYm/garLQOw/4K3BKZQMppfERsRdwaX7dicBPU0p/L6kzPCLaA78iW2/6VWCvlNKEmnghJEmStPJZ0es8Pw7EMqrsUaCNBcDx+ba0Ou8D+1TTzhPAgGrqXE0WwiVJkqT6PedZkiRJqk8Mz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGFwnNEvBsR7as4vmZEvFvz3ZIkSZLqn6Ijzz2AhlUcbwp0rbHeSJIkSfVYo2UVRsT+Jbt7R8TMkv2GwK7Ae7XQL0mSJKneWWZ4Bu7MvybghrKyhWTB+aQa7pMkSZJULy0zPKeUGgBExHhgy5TSRyukV5IkSVI9VN3IMwAppfVquyOSJElSfVcoPANExNZkc5w7UnajYUrppzXcL0mSJKneKRSeI+Jk4ALgbWAi2RzoSqnKkyRJkqRVTNGR5xOAn6aU/lCbnZEkSZLqs6LrPLcG7qvNjkiSJEn1XdHwfBvwzdrsiCRJklTfFZ228QFwdkR8A3iZbI3nz6WULqnpjkmSJEn1TdHwfCQwG9g230olwPAsSZKkVZ7rPEuSJEkFFZ3z/LmI6BQRy32eJEmStLIrFIIjonFEXBARnwIfAj3y47+PiB/XYv8kSZKkeqPoCPKZwL7A94AFJcdHAofVcJ8kSZKkeqnoDYNDgSNSSk9EREXJ8VeBXjXfLUmSJKn+KTry3AWYUMXxRhQP4JIkSdJKrWh4fg3YoYrjBwKja647kiRJUv1VdNT4bOCWiFgXaAj8v4joDRwM7F1bnZMkSZLqk0Ijzymle8lGmXcHKshuINwQ2Del9EjtdU+SJEmqPwrPV04pPQg8WIt9kSRJkuq15b7ZLyKaUTZinVKaW2M9kiRJkuqpoh+S0j0i7omIWcAc4NOyTZIkSVrlFR15vgVoBhwPTAFSrfVIkiRJqqeKhuf+wJYppddrszOSJElSfVZ0necxQIfa7IgkSZJU3xUdeT4auCIiriD7SO6FpYUppfdrumOSJElSfVM0PDcAOgF3s+R858j3G9ZwvyRJkqR6p2h4/gswFTgNbxiUJEnSaqpoeO4NbJ5SerM2OyNJkiTVZ0VvGBwJrFebHZEkSZLqu6Lh+Rrgsog4MiK2jogBpVvRi0XEDhHxz4j4MCJSRBxWUtY4In4fES9HxJyImBQRt0ZEt7I2Hs/PLd1uL6vTNiJujoiZ+XZzRKxZVmfTiHgiIubl/fm/iIiyOkMiYmxELMi/7lf0uUqSJGnVU3Taxm351+uqKFueGwZbka3WcVO+lWoBDADOA14C2gAXAw9ExGYppUUldf8MnF6yP6+srVuBbsA38/0/ATcD+wJERGvgYeBJYEuyaSl/Jvv0xIvzOoOA4cCZwF3A/sAdEfGNlNJzBZ+vJEmSViFFw3ONTNlIKd0H3AcQEcPKymYCg0uPRcQxwGtAH+CVkqK5KaXJVV0jIvqQhebtUkrPlLTzVERslFIaBxxCFtYPTSnNA16NiN7AiRFxSUopAT8DHkspnZc3fV5E7JwfH/rVXgFJkiStzApN20gpTVjWVov9a51/nVF2/LsR8VFEvBYRF0XEGiVlg4DZwNMlx/5LNqq8bUmdp/LgXOlBoAvQo6TOQ2XXfbCkDUmSJK1mis55JiL2jIgR+dzfdfNjR0bErrXRsYhoQjaF4t6U0v9Kim4lGzneGfgNMAT4e0l5Z2BaPnoMQP54al5WWWdK2SWnlJQtq05nqhARR0fEqIgYNW3atOqfoCRJklY6hcJzRBwC/A14i2wKR+O8qCFwak13KiIaAbcAawKHl5allK5LKT2YUnolpXQ7cBAweHluXKwNeb8GppQGdujgJ5lLkiStioqOPJ8KHJVS+jlQeuPes8DmNdmhPDjfBmwG7JpS+riaU0YBi4EN8/3JQIfSlTPyxx3zsso6ncra6VRStqw6Vc61liRJ0qqvaHjeEHimiuOz+WJe8tcWEY3JVrjYDNh5aTcFltmUbAR8Ur7/DNmqHoNK6gwCWvLFPOhngO0jollJncHAROC9kjpL3MCY7z+NJEmSVktFV9uYCPQCym8O3AF4p+jFIqIVsEG+2wDoFhGbA9Pza9xBtnTcvkCKiMr5xTNTSvMioifZfOf7gI+AjcnmRb9IdlMgKaXXI+IB4NqIODo//1pgRL7SBmTzps8EhkXEuflz+wVwdslc6cuBJyPiF8A/gP3I5llvV/T5SpIkadVSdOT5OuCKiPhGvr9uRBwKXED2ASpFDSQLui8CzYGz88fnAOsA3yZb8WI02Uhy5XZQfv5nwK5kq16MA64gWxFjt5TS4pLrHAyMyes9mD/+fmVhybJ4XcimfVxFFsIvKanzNPBd4DDgZeAHwEGu8SxJkrT6KjTynFK6ICLakH2wSDPgMWABcFFK6aqiF0spPQ7EMqosq4yU0gfAjgWuMwP4XjV1XiEbOV9WnTuBO6u7niRJklYPRadtkFI6IyLOI5sq0QAYm1KaXWs9kyRJkuqZwuEZIKU0l2yagyRJkrTaKRSeI+IxIFVRlID5wNvAX1JKL9Rg3yRJkqR6pegNg68DA8husPtfvq2dH5sKbA88V1ufNihJkiTVB0WnbcwHhqWUflZ6MCIuJvv06wERcTlwLvDvmu2iJEmSVD8UHXk+lGw5t3LX8sXHZ19PdjOhJEmStEoqGp4D2KSK4xvzxfJynwEVNdEpSZIkqT4qOm3jL8ANEbEh8Hx+bEvgNGBYvr8j8GqN9k6SJEmqR4qG55OBKcDPgcqPzJ4MXAhclO8/CNxfo72TJEmS6pGinzC4GPgd8LuIaJ0fm1VW5/2a754kSZJUfyzXh6TAl0OzJEmStLooHJ4j4nBgKNANaFJallJav4b7JUmSJNU7hVbbiIhTgIuB0UAP4B9kNwe2A26spb5JkiRJ9UrRpeqOAo5OKf0SWAj8IaX0LbJA3b22OidJkiTVJ0XD8zrAyPzxPKB1/vg2YEhNd0qSJEmqj4qG58nAWvnjCcCg/PEGQKrpTkmSJEn1UdHw/CjwrfzxDcAlEfEYMBy4qzY6JkmSJNU3RVfbOJo8aKeU/hgRM4BvAH8Hrq2lvkmSJEn1StEPSakAKkr2h5ONOkuSJEmrjULhOSJ2WEpRAuYD76SUptdYryRJkqR6qOi0jcf54sbAyL+W7ldExD+B76eU5tRc9yRJkqT6o+gNg3sDrwPfI1thY4P88WtkS9UNATYHflfzXZQkSZLqh6Ijz+cCJ6SU/l1y7N2ImAb8PqW0RUQsBq4Ejq/pTkqSJEn1QdGR542BD6s4/mFeBvAK0LkmOiVJkiTVR0XD81jgjIhoWnkgf3x6XgawLtmHqUiSJEmrpKLTNn4M3At8GBGv5sf6ki1ft0++vz5wdc12T5IkSao/iq7z/FxErEd2k+BG+eFbgVtTSrPzOjfVThclSZKk+qHoyDP5EnR+mqAkSZJWW4XDc0SsA+wAdKRsrnRK6ZIa7pckSZJU7xT9hMFDgBuBRcA0vviAFPLHhmdJkiSt8oqOPJ8DXAz8OqW0uBb7I0mSJNVbRZeq6wT8yeAsSZKk1VnR8HwfsHVtdkSSJEmq74pO23gY+H1EbEL2SYILSwtTSnfVdMckSZKk+qZoeK5cou70KsoS0LBmuiNJkiTVX0U/JKXo9A5JkiRplWUoliRJkgpa5shzRDwN7JVS+iTfPx+4MKU0Pd9fC3ghpdSttjsqSZJWTqNHj27SqFGj64HtcKqn6reKiJi8aNGiswcMGPBgVRWqm7axDdCkZP8nwPXA9Hy/IdD1a3dTkiStsho0aPCj1q1bf6N79+6fNGjQIFV/hlQ3KioqYt68eW3ee++9P7zwwgvHVRWgl3faRtRQ3yRJ0mqiYcOGh3fp0mWOwVn1XYMGDVLLli3n9ejR47NGjRqdWWWdFd0pSZK0ekkptWnSpMnC6mtK9UPz5s3np5Q6V1VWXXhO+VZ+TJIkqaiI8M1rrTzyd0mqzMnVzXkO4JaIWJDvNwOuj4i5+X7TmumiJEmSVP9VN/L8F2Ai8HG+3QJ8ULI/EbipNjsoSZK0Opg0aVKjiNhixIgRa9R1X+qrE088scuGG264SV32YZkjzymlw1dURyRJ0urjpHbrb7Eir3fx9HdHL0/9IUOG9Jg+fXqjxx577O3S408++WSLHXfcsc8bb7zxykYbbfRZzfbyq/drWbbaaquNevfuPe+mm256vzb7VpPGjRvXpHfv3ps+8cQTr++www6VMx4488wzJ5966qlT6rJv3jAoSZK0As2fP3+lnABeH/rdpk2bis6dOy+uyz4YniVJkr6iRYsWceCBB3bv2rXrps2aNRvQvXv3vr/61a86LV78Rb4bMmRIj5133nmDM844o3OnTp0269Kly2YATzzxRItNNtmkT9OmTQf06dNn4yeeeKLl8l6/su3f/OY3HTt27LhZ69atNz/ggAN6fPrppw0qy59//vlWN998c4eI2CIithg3blwTgNGjRzfbaaedNmjZsmX/du3a9dt3333Xe//99xuVt13a7+OOO67rJpts0qe8H/379+992GGHrVu5f/nll7fv2bPnJk2bNh3Qo0ePvmeffXbH0tckIra46KKL1tpzzz3Xb968ef911lln06uvvrpdZXnv3r03Bdhxxx37RMQWW2211Ubw5Wkbixcv5pRTTlm7c+fOmzVp0mRAr169Nr7lllvWrCwfN25ck4jYYtiwYWtuu+22GzZv3rx/z549N7n77rtbL+9rXcnwLEmS9BUtXrw4unTpsvDWW299Z8yYMa/++te//vDyyy9f+4orrlirtN7IkSPXeOWVV5qPGDHizQceeODNmTNnNthvv/027Nat24L//Oc/Y88999z/nX766et8lT6MGjWq1Wuvvdb8gQceeHPYsGHvPvjgg2v+9re/7Qhw3XXXfbD55pvPOeCAAz6eMGHCmAkTJozp2bPnZxMmTGi82267bdSnT595Tz311Ov333//m3PmzGm4zz77bFAacsv7ffjhh388duzYFi+++GKzyjpjx45t8tJLL7U89NBDPwa4+OKL1zr33HO7/upXv5o4ZsyYV3/7299+cOWVV679+9//vkNpvy+88MIu++677yfPP//82G9/+9vTTzjhhB5vvfVWE4DHH3/8dYA777zzrQkTJoy59957q5ymcu6553a85pprOp911ln/GzVq1Gt77bXXJ4ceemjPp59+unlpvbPPPrvrcccdN3XkyJFj+/XrN+fwww9ff+bMmV8pB1e32oYkSdJq6amnnmrTokWL/qXHUlpyxd6mTZumyy67bGLl/kYbbfTZCy+80OKOO+5o9/Of//yjyuNNmjSpGD58+HvNmzdPABdddNFaCxcujNtvv/29Nm3aVGy55Zbzp0yZMuknP/nJesvbz5YtW1bccsstExo1asSAAQPm/+1vf5vxxBNPtAYmt2/ffnHjxo1T8+bNK7p167ao8pxLL720Q+/eveddc801H1YeGz58+PiOHTtu/uSTT7bYeeed51bVb4A+ffrMHTZsWLv+/ftPBBg2bFj77t27L6g856KLLlr7rLPO+t/hhx8+A6B3796fvf3225NuuOGGjqeffvq0ynYOOOCAj3/84x9PB7jssss+vPHGGzs+/PDDrTbccMPpnTt3XgTQoUOHRaX9LnfVVVd1PvbYYycfe+yxle1MfPrpp9f4/e9/3/mee+4ZX1nvxz/+8ZSDDz54JsDFF1/84frrr9/+2WefbbHHHnvMXt7X2/AsSZJUhYEDB376pz/9aULpsRdffLH5D37wg56lxy644IION91001offvhhkwULFjRYtGhRdOnSZYmbCXv16jWvNIC+/vrrzTfaaKN5bdq0qag8tuOOO875Kv3cYIMN5jVq9EWkW3vttRe+8MILy5wC8tJLL7V4/vnnW5X/cQDw5ptvNqsMwuX9BjjwwAOn33DDDR0uv/zyiQB33nlnuwMOOOBjgIkTJzaaPHlyk5NPPrn7Kaec0r3ynMWLF0f5Hx6bbbbZvMrHjRs3pm3btoumTp3auOjznj59eoNp06Y13n777ZcIwNtss82njzzySJvSY/379//8Wt27d18IMHny5K+UgwufFBGbAscAPYEjUkqTIuI7wISU0otf5eKSJEn1VfPmzSv69u27oPTY9OnTG5buX3/99W1//etfr3vmmWd+sMMOO8xZc801F1966aUdH3jggTVL67Vo0aKCWtK4ceMlUmlEkFJa5s19FRUVsdNOO8287LLL/lde1rVr188/DbKqfh9++OHTzz333HUeeeSRls2aNUvjx49vdsQRR3wM2RxkgAsvvPD9nXbaaZmjuk2aNPlSvysqauZlKv9QntJrNWiQzdaoqKj4SjdAFprrERG7A88DXYFdgMp5JD2BKj/3eynt7BAR/4yIDyMiRcRhZeUREWdFxMSImBcRj0fEJmV12kbEzRExM99ujog1y+psGhFP5G18GBH/V/7RRhExJCLGRsSC/Ot+y9sXSZK0evvPf/7TarPNNptz+umnT9tuu+3m9u3bd8H48eOr/RC5Pn36zHvzzTebz5o16/Ms9uSTTy73DYNFNG7cuKJ0HjNAv3795r711lvNN9xww8/69u27oHRr27btMhNs9+7dF2699dazbrrppvZ/+ctf2m2++eZzNt54488A1l133UUdOnRY+M477zQtb7f8D5Fladq0aQJYtGjRUgNuu3btKjp06LDwqaeealV6/Nlnn11jww03nF/0Wsur6ETp3wAnppT2A0rfhngc2Go5rtcKeBU4AZhXRfmpwEnA8cCWwFTg4YgoXSz8VmAA8M18GwDcXFkYEa2Bh4EpeRsnAKcAJ5bUGQQMB/4KbJ5/vSMitl7OvkiSpNVYr169FowdO7bF3/72t9avvPJK01NOOWXt559/vlV15x111FHTGzZsmIYOHdpj1KhRze6+++7WF1100dq10cdu3bp99tJLL7UcN25ck0mTJjVavHgxJ5100tRPP/204b777rv+o48+2nLs2LFN/vGPf6wxdOjQ7jNmzKg2Hw4dOnT6vffe2/aee+5pd9BBB31cWvaLX/xi4jXXXNP57LPP7jhmzJimzz//fLM//OEP7X/5y192Ltrnrl27LmzWrFnFfffd1/qDDz5o9PHHHzesqt5xxx03+Y9//GPna6+9tt3LL7/c9Gc/+1mX0aNHtzrttNMmF73W8io6baMvcF8Vx6cD7ao4XqWU0n2V7UTEsNKyfGT4Z8DvUkp/z48dShZaDwaujYg+ZIF5u5TSM3mdY4CnImKjlNI44BCgBXBoSmke8GpE9AZOjIhLUjbh5mfAYyml8/LLnxcRO+fHhxbpS9HnXBNOarf+irzcKuvi6e/WdRckSauYk046adqYMWOaH3nkkeunlNhzzz1nHHPMMVNuu+22tZZ1Xps2bSr+/ve/v33cccd123bbbTdeb7315v/mN7/53yGHHLJBTffxl7/85eQf/OAH622++eabzJ8/v0HlB7w88cQTb5xyyildv/Od72z42WefNejcufNnO+6448zyOc5V+f73vz/jlFNO6TZ79uw47LDDppeWnXjiiR+1bNmy4vLLL+/029/+dp2mTZtWbLjhhvOOOeaYqUX73LhxY84777wPLrzwwrUvvfTSLltsscXskSNHjiuvd8YZZ0z99NNPG5555pnrfPzxx43WW2+9+cOGDXtn0KBBVQ3S1ogvTd6uslLEB8B3U0r/jYhPgX4ppXcjYgjw+5TScn+jI2I2cFxKaVi+vz7wDrBVSun5knr/Aj5KKR0aEUcAlwOt8xBcGbo/BY5PKf05Im4C2qeU9i5pY0tgJLB+Sml8RLwPXJlSurCkzil5f7oX6cuyntvAgQPTqFGjlvclWSrDc80wPNeA2/rWdQ9WfkNfreserPQ6/fiMuu7CSm/K1edVX2n5LfXt9TFjxrzXr1+/j5ZWLtVHY8aMWatfv349yo8XnbZxK3BhRKwDJKBRROwIXATcVEN9rBzKL//IxSklZZ2Baakk8eePp5bVqaoNCtTpXFZvWXWWEBFHR8SoiBg1bdq0qqpIkiRpJVc0PP8KGA9MIJu3PBZ4FPgPUCt/vq5sUkrXpZQGppQGdujQofoTJEmStNIpNOc5pbQQOCQi/g/oTxa6X0wpvVWDfamc2N0JeL/keKeSsslAh4iIsmkbHcvqdCpru1NJ2bLqTC6rt6y+SJIkaTWzXB9LmFJ6J6V0Z0rpbzUcnCEb2Z4MDK48EBHNgO2Bp/NDz5CNfA8qOW8Q0LKszvb5uZUGAxOB90rqDGZJg0vaKNIXSZIkrWYKjTxHxI1LKUrAfOBtYHhKaeJS6lW20wqovLmwAdAtIjYHpqeU3o+Iy4DTI+IN4E2y6SKzyeZck1J6PSIeIFt54+i8nWuBEflKG+R1zwSGRcS5QC/gF8DZJXOlLweejIhfAP8A9gN2BrbLr5Oq64skSZJWP0WXqutANupaQbZOM2TL1wUwGtgfOCcitk8pvbSMdgYCj5Xsn51vfwEOAy4g+wCWq4C2wHPA7imlT0vOORi4Engw3/8ncFxlYUppZkQMztsYBcwALgYuKanzdER8FzgXOIdsZY2DUkrPlVynSF8kSZK0Gikanv9LNur6w5TSXICIaAFcD4wB9iJbdeNiYNelNZJSepxlLGWTjwyflW9LqzMD+N6yOptSegXYoZo6dwJ3fp2+SJIkafVSdM7zCcA5lcEZIH98HvDzlNJnwO/JPq1PkiRJWiUVDc+tgKo+MrJzXgYwi+Ij2ZIkSdJKp2h4vhu4ISL+X0T0yLf/B9wA3JXX2YrsxjpJkiQBt9xyy5rdu3fv26hRoy2GDBnSo677Ux+MGDFijYjYYtKkSSvloGvRTh9LdsPdLSXnLAJuBE7O918HjqrR3kmSpFXTbX23WKHXG/rq6OWpPnHixEannnpql8cee6zNtGnTGq+xxhqLe/XqNe/UU0+dvN9++80q2s7xxx/ffejQoR+dcsopU1u3bl0xZMiQHtOnT2/02GOPvb2s80488cQu9957b9u33nrrtar2VxZdu3bd9Ic//OHUc8455/NPbd5tt91mT5gwYUynTp0W1WXfvqqiH5IyFzg2Ik4CeuaH30kpzSmp81LNd0+SJGnF+9a3vtVz/vz5Da6++ur3+vTps2DSpEmN/v3vf68xbdq0hkXb+Oijjxp+8sknjfbcc89Z66233sLa7G9RixcvJqVEo0Z1N+jbrFmz1K1bt5UyOMPyf0jKnJTSy/k2p/ozJEmSVi4fffRRw9GjR7c699xz//ftb3/70169en224447zj3nnHOmHH300TMq602bNq3h/vvv36N169abN2vWbMC2227ba9SoUc0gm5rQoUOHzQG+9a1v9YqILbbaaquN7rrrrvaPP/54m4jYIiK2GDFixBrV9eeKK65of+mll6799ttvN6s874orrmgP8PHHHzccOnRo93bt2vVr2bJl/y233HKjJ598skXpuS1atOg/fPjwNhtuuOEmTZs23eJ3v/tdx8aNGw+YPHnyEn8IHHfccV179eq1ceX+ww8/3HLLLbfcqHnz5v07duy42SGHHNJt+vTpn2fHrbbaaqPvfe973Y477riubdu27deuXbt+Rx999DqLFy/+vHzixIlNfvOb36xT2e/K16Z82sZf/vKXNXv16rVxkyZNBnTu3Hmz0047rXNFRcXnfevateump5566toHH3xw91atWvXv1KnTZr/+9a/LPy16hSgcniNi54i4LiIeiIhHS7fa7KAkSdKK1KZNm8UtWrSo+Mc//rHm3Llzl7rE7ne/+90eL774Ysvhw4e//eSTT77erFmzin322afX7NmzY7fddps9atSo1wCGDRv2zoQJE8Y88MADb+21114zBg0aNGvChAljJkyYMGa33XabXV1/jjjiiOlHHXXUlB49esyvPO+II46YXlFRwe67777BpEmTGv/9739/67nnnhu77bbbfrrXXnttNGHChMaV53/22WcNzj///LWvuuqq91544YVXf/SjH33ctm3bRTfddFO7yjoVFRXcfffd7Q488MCPAUaOHNn829/+dq+99trrk5EjR469/fbb33nttddaHHzwwT1K+3bPPfe0a9SoUXriiSfeuOCCC96/8cYbO/3pT39qB3Dvvfe+3alTp4U/+9nPJlX2u6rn99RTT7U44ogjeu69996fjBo16rUzzzzzf1deeeXa559/fsfSetddd12nvn37zn322WfH/vSnP5187rnnrvPII4+0rO71q2mFwnNEHAbcD6wB7ARMI/vgkAHA2FrqmyRJ0grXuHFjrrrqqvF33313+7Zt2/bffPPNex999NHrPProo58HtVdeeaXpo48+uubVV189Yc8995y91VZbzbvjjjvGz5kzp8F1113XvlmzZqlLly6LANq3b7+4W7dui9q1a1fRrFmziqZNm6Zu3bot6tat26JmzZqlpfck06pVq9SqVauKRo0aUXleq1at0ogRI9Z4/fXXW4wYMeKdnXfeeW7fvn0XXH755RPXWWedBddff/3nwXjx4sVceeWV7+++++5zNttsswXt27df/K1vfWv68OHDP6/z8MMPt5o8eXKTww8/fDrA+eef32mfffaZfvbZZ0/ZdNNNF+yyyy5zrr766gkPPvhg2w8//PDzEeOePXvOv+yyyyZuttlmC4488sgZW2211axHH310DYBOnTotbtiwYVpjjTUWV/a7qud34YUXdtpyyy0/vfTSSyduttlmC370ox9NP/bYY6dcccUVnUvrbb/99jNPP/30aX379l1wxhlnTO3WrduChx56qHXx72zNKDryfDJwXEppKLAQ+GVKqT/ZDYTV/sUkSZK0MjnssMM+mTRp0pjbb7/97d12223m888/32rXXXft/Ytf/KIzwMsvv9ysQYMG7LLLLp9PY23fvv3iXr16zRs7dmyzFdHH559/vsX8+fMbdOzYcfMWLVr0r9zefvvt5u++++7nfWjYsGEaNGjQ3NJzDzvssOkvvvhiqzfffLMJwE033dRuyy23/LRnz54LAV555ZWW//jHP9qXtrvrrrv2Bnj99debVraz8cYbzyttt3PnzgunTZvWmOXw9ttvN9t6662XyJM77LDDp1OnTm1cOk2kb9++S1yrY8eOC6dOnbrCJ28XveD6wCP54wV8sbbzH4DHgV/UbLckSZLqVosWLdJ+++03K19dY9JBBx3U/eKLL+5y1llnTVnWeRFLnelRoyoqKqJ9+/YLH3vssXHlZW3btl1c+bhJkyap/AbB7bbbbu566603/89//nO7s846a8p9993X9swzz/xfSdscdNBBH5122mlfeq49evT4rPJxo0aNlhg5jwiyD2muGaWvZePGjb90rYqKihXzYpcoGp4/JpuyAfAh0Bd4GWgPNK+FfkmSJNUrffr0mb948eKYO3dubLbZZvMrKip49NFHW+65556zAaZPn97gzTffbP69733v46W10aRJk7R48eLlDnxNmjSpqLwRr9LAgQPnnn/++Y0bNGiQNt5448+WcupS/b//9/8+vvPOO9tvuumm8+bNm9fwBz/4wec3Q/bt23fuuHHjmvft23fB8rZbqnHjxtU+3w022GD+c88916r02JNPPrlGp06dFrZt27ZiaefVlaLTNp4Cds8f/w24IiL+DNwGPFwbHZMkSaoLkydPbrjNNtv0uvrqq9s999xzzd94440mN954Y9s//OEPnbfZZptZ7dq1q9h0000X7Lrrrp/85Cc/6f7AAw+0GjlyZPMDDjhg/ZYtW1YcddRRSw3P3bt3X/Dmm282HzNmTNNJkyY1WrBgQaEg3aNHj88mTZrU5D//+U+LSZMmNZo3b158+9vfntW/f//Z3/nOdzb429/+1vqNN95o8sgjj7T8+c9/3uWBBx5oVV2bRxxxxPR33nmn2TnnnNN1l112+aRdu3afB9Uzzjhj8iuvvNLi4IMP7vbf//63+auvvtr0tttua3PwwQd3L/YqZrp27brg6aefbjV+/PjGS/tQlFNPPXXK888/v8aJJ57Y5eWXX256zTXXtLv22ms7HX/88ZOX51orStHwfBxZUAY4H7iQbNT5b8CRtdAvSZKkOtGmTZuKgQMHzrn66qs7DR48eKP+/ftvcvbZZ3f9zne+8/Hdd9/9bmW922677b3NN998zoEHHrjBDjvs0Gf+/PkNRowY8WarVq2WOm/hpz/96Uc9e/act+22227cpUuXfg8//HC1IRfg0EMPnbHjjjvO3GuvvXp16dKl3/XXX9+uQYMGPPLII2994xvf+PT444/vsemmm/b93ve+1/Ott95qtu6661a7rnSvXr0+GzBgwOxx48Y1P+SQQ5YI/FtvvfW8Bx98cNwHH3zQdI899ui95ZZbbnzmmWd27dix43KtV33OOedMnDhxYpPevXtv2qVLl35V1dluu+3m3njjje+MGDFizS222GKTs88+e53jjjtu0i9/+cupy3OtFSWqm5cSEY2Ao4F/pJQmrpBereQGDhyYRo0aVWPtndRu/Rpra3V28fR3q6+kZbutb133YOU39NW67sFKr9OPz6jrLqz0plx9Xm00u9QR1DFjxrzXr1+/j2rjolJtGTNmzFr9+vXrUX682pHnlNIispHm5bpzUpIkSVrVFJ228SywYj+DXpIkSapniq62cT1wUUR0A0YDS3w0d0rphZrumCRJklTfFA3Pt+ZfL6miLAENqzguSZIkrVKKhuf1arUXkiRJ0kqgUHhOKU2o7Y5IkiRJ9V3RGwaJiD0jYkREjI2IdfNjR0bErrXXPUmSJKn+KBSeI+IQsg9EeYtsCkflsnUNgVNrp2uSJElS/VJ05PlU4KiU0s+BRSXHnwU2r+lOSZIkSfVR0fC8IfBMFcdnA61rrjuSJEla1UXEFn/+85/b1nU/voqiq21MBHoB5TcO7gC8U6M9kiRJq7xOPz5jhX742pSrzxu9PPWHDBnS46677mp/8sknT7zwwgsnVR4fMWLEGvvuu2+viRMnjll77bUXLauNpRk3blyT3r17b/rEE0+8vsMOO8wtck5NXLcuDBkypMf06dMbPfbYY2+XHp8wYcKYDh06LK6rfn0dRUeerwOuiIhv5PvrRsShwAXANbXSM0mSpDrUtGnTdM0113SeOHFi0cHGlcKCBQuirvvQrVu3Rc2bN0913Y+volB4TildANwFPAy0BB4D/gj8MaV0Ve11T5IkqW5svfXWs7p06bLg9NNPX3tZ9e6///5Wm222We+mTZsOaN++fb8f/vCH686fP79wQB03blyTiNhi2LBha2677bYbNm/evH/Pnj03ufvuu1tXlu+77769ALp06dIvIrYYMmRID4CKigp+9atfdVp33XX7NmvWbECvXr02vvrqq9uVt33ttde222abbXo1a9ZswIUXXtihWbNmA2699dY2pf246667Wjdq1GjAhx9+2Ahg/PjxjffZZ5/1W7duvXnr1q0332mnnTZ45ZVXmlbWP/HEE7tsuOGGm1x33XVt11133b4tW7bsv9tuu/WcNGlSo8ryu+66q/3jjz/eJiK2iIgtRowYsQZ8edrGyJEjm2+77ba9mjVrNqBNmzabDxkypMfHH3/8+YfwDRkypMfOO++8wW9+85uOHTt23Kx169abH3DAAT0+/fTTwivH1ZTCF0wpnQGsBWwFbAN0SCn9urY6JkmSVJcaNGjAb37zmw//+te/dnjttdeaVlVn/Pjxjffff/8N+/btO/eZZ54Z+4c//OG9e+65p93xxx/fdXmvd/bZZ3c97rjjpo4cOXJsv3795hx++OHrz5w5s0HPnj0/GzZs2DsAo0aNem3ChAljrrvuug8ATjjhhK633HJLh0svvfT9l1566dUTTzxx8kknndT99ttvXyIYn3POOV2POeaYaS+99NKrQ4cOnbHLLrt8cuutt7YvrXPLLbe0+8Y3vjGra9euiz799NMGO++880ZNmzatePjhh8c98cQTb3Tq1GnhHnvs0as0sH744YdN7rjjjnZ33nnnO/fcc8+bY8eObXHSSSd1BTjzzDMn77XXXjMGDRo0a8KECWMmTJgwZrfddptd/rxnzZrVYJ999tmwRYsWi5988snXb7311rdHjx7d6uCDD+5RWm/UqFGtXnvtteYPPPDAm8OGDXv3wQcfXPO3v/1tx+V9nb+uQm9DRMTPgFtTSlOBUbXaI0mSpHrioIMOmnnJJZfMPu2007qOGDHi3fLySy65pGPHjh0X3nzzze83bNiQAQMGzJ82bdr/Tj755O6XXHLJxDXWWKOi6LV+/OMfTzn44INnAlx88cUfrr/++u2fffbZFnvsscfs9u3bLwbo0qXLoso5z7NmzWpw/fXXd/rHP/7x5je/+c3ZAL17954+cuTIltdcc02H7373uzMr2z7yyCOnHn744TMq9w855JCPjzzyyPVnzJjRoG3bthWzZ8+Ohx56qO1FF100AeCGG25om1LijjvueK9Bgywr//Wvf52w1lprbT58+PA2Rx555AyAxYsXx+233/5eZf++//3vT7vtttvWAmjTpk1Fs2bNKpo2bdqgW7duS52nff3117ebN29egzvuuGN827ZtKyD7gL59992316uvvtq0b9++CwBatmxZccstt0xo1KgRAwYMmP+3v/1txhNPPNEamFz0Na4JRUeeTwT+FxEPRMQhEdGiNjslSZJUX/zud7/73/3339/2qaee+lL+GTduXLP+/fvPadjw8xkG7LLLLrMXLlwYY8eOrXK0emn69+8/r/Jx9+7dFwJMnjx5qQOdL774YrMFCxbEfvvtt2GLFi36V2633HJLhwkTJjQrrbvVVlvNKd0/4IADZjVr1qzir3/9a1uA2267bc2UEocccsgnAKNHj2754YcfNm3VqtXn7bZu3br/rFmzGr7zzjufP6+11177s8rgDNClS5eF06dPb8xyeP3115v16tVrXmVwBthtt91mN2jQgDFjxnz+PDbYYIN5jRp98XKsvfbaCz/66KMVPh+96AW7AzsBBwNXAn+MiHuAW4CHUkqF/6qSJElamey8885z99hjjxmnnnrqOmecccak6s/IRCzffXlNmjT5/Aa6ytHeioqKpTayePHiABg+fPjb66+//mdLawugfAS8adOmae+9955x++23tzvuuOM+vu2229rvvvvuMyrrVVRU0Lt377m33377l0bbO3To8PkocuPGjZe4TkRQUVFzsbD0NazqWimlFX7zY6HwnFJKZDcJPhYRPwH2IQvSdwGfAF1qq4OSJEl17YILLviwf//+m9x3331LfL7FRhttNP+f//xn28WLF1M5+vzoo4+2aty4cerTp8+Cmrp+06ZNKwAWLfpi9kP//v3nNWnSJI0fP77Jt771rU+Xt81DDz304z333LP36NGjmz311FOthw8f/vlycgMGDJj7z3/+s13nzp0XrbXWWl95SbkmTZqkypC/NH369Jk/fPjwtSqnkAA88sgjrSoqKthss83mf9Vr15blvkMxpfQZ2QemPEO27nPnmu6UJElSfdK3b98FQ4cO/ejGG2/sVHr8xBNPnDp16tTG3//+97u98MILzW6//fY255xzzjqHHnro1OWZ71ydDTbY4LOI4O9//3ubiRMnNpo5c2aDtm3bVhxzzDGTzzzzzHUvu+yy9q+++mrTp59+uvkFF1zQ4aKLLlqrujYHDx48Z+21115wyCGHrN+2bdtF3/rWt2ZVlh199NHT27dvv2jPPffc4F//+lerN954o8n999/f6qijjlqndMWN6nTv3n3Bm2++2XzMmDFNJ02a1KiqZfKOPvro6c2bN6848MAD1xs5cmTz+++/v9Vxxx3Xfffdd/+kcr5zfVI4PEfEGhFxeEQ8ArwPHAncCvSsrc5JkiTVF+eff/7Ehg0bLjF1YL311lt41113vfXqq6+22GabbTb+yU9+0uPb3/729CuuuOLDmrz2euutt/Ckk06aeN5553VdZ511+h1xxBHdAC677LKJp5xyysQrr7yy84ABAzbZe++9e/3jH/9Ys2fPnp9V1ybAkCFDpo8bN675t771reml84nXWGONiqeeeuqN7t27L/j+97/fc7PNNut75JFHrvfJJ580Wp6R6J/+9Kcf9ezZc9622267cZcuXfo9/PDDrcrrrLHGGhUjRox4a/bs2Q132GGHPgcddNAGW2yxxexbb731vaLXWZEim5FRTaWIO4G9gFnAcOCWlNLztdy3ldbAgQPTqFE1tyjJSe3Wr7G2VmcXT//StC0tr9v61nUPVn5DX63rHqz0Ov34jLruwkpvytXn1UazS31rfsyYMe/169fvo9q4qFRbxowZs1a/fv16lB8vesPgAmAI2c2BS/y1ERG7pZQe+fpdlCRJkuq3ojcMHlK6HxFdgcOBI8hW4mhY1XmSJEnSqmR55jw3jIj9I+I+4D1gP7KP6N6glvomSZIk1SvVjjxHxEZkNwf+AJhDdpPgYOD7KaWxtds9SZIkqf5Y5shzRDwFPAu0BQ5MKa2fUvrVCumZJElaVaQiCxRI9UX+4TRVLjVY3bSNQcBNwKUppSdqumOSJGnVFxEzP/vss+X6yGapLs2bN69ZREyuqqy68Lwl2dSO/0TEixHx84jwQ1EkSVJhixcv/vPEiRNbLuujpqX6oKKiIubMmdP8vffea7Jo0aKzq6qzzDnPKaUXgZ9ExEnA/yNbXeMCstC9d0RMSinNqOmOS5KkVUdFRcU1s2bNGvDKK69shyt0qX6riIjJixYtOnvAgAEPVlWh6FJ184GbgZsjYgOyGwh/DpwbEY+mlPassS5LkqRVyhZbbPEZcGhd90OqCYWXqquUUno7pfQLYF3gQKDQxz9KkiRJK7uinzD4JfknDd6Tb5IkSdIqb7lHniVJkqTVleFZkiRJKsjwLEmSJBVkeJYkSZIKqlfhOSLei4hUxfavvPysKsoml7UReb2JETEvIh6PiE3K6rSNiJsjYma+3RwRa5bV2TQinsjb+DAi/i8iXNxdkiRpNVavwjPZJxquXbINABLwt5I648rqbFrWxqnAScDxeXtTgYcjYo2SOrfmbX8z3waQrWMNQES0Bh4GpuRtnACcApxYA89RkiRJK6mvvFRdbUgpTSvdj4gfArNYMjwvSilV+Vnj+cjwz4DfpZT+nh87lCxAHwxcGxF9yALzdimlZ/I6xwBPRcRGKaVxwCFAC+DQlNI84NWI6A2cGBGXpJRSjT1pSZIkrTTq28jz5/Ig/EPgljzAVlo/n5IxPiJuj4j1S8rWAzoDD1UeyM99Etg2PzQImA08XXLef4E5ZXWeKrvug0AXoMfXfW6SJElaOdXb8AwMJgvD15ccew44jGzk+CiyoPx0RLTPyzvnX6eUtTWlpKwzMK109Dh/PLWsTlVtlF5DkiRJq5l6NW2jzFHA8ymlMZUHUkr3l1aIiGeBd4FDgUtWbPeWFBFHA0cDdOvWrS67IkmSpFpSL0eeI6Ij8G2WHHX+kpTSbOA1YMP8UOVc6E5lVTuVlE0GOpSunJE/7lhWp6o2Sq9R3pfrUkoDU0oDO3TosKxuS5IkaSVVL8Mz2dSMBcBty6oUEc2A3sCk/NB4snA7uKzO9nwxx/kZoBXZvOZKg4CWZXW2z8+tNBiYCLy3vE9GkiRJq4Z6F57zUeAjgdvzkeXSsosiYseIWC8itgbuJAu9f4HP5y5fBpwWEftHRF9gGNkNgrfmdV4HHiBbeWNQRAwCrgVG5CttkNedCwyLiL4RsT/wC8CVNiRJklZj9XHO805k0zC+V0XZOmSj0WsB04BngW1SShNK6lwANAeuAtqS3WS4e0rp05I6BwNXkq2gAfBP4LjKwpTSzIgYnLcxCpgBXEwdz6uWJElS3ap34Tml9BhQ5Sf5pZS+W+D8BJyVb0urM4Oqw3lpnVeAHaq7niRJklYf9W7ahiRJklRfGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkH1KjxHxFkRkcq2ySXlkdeZGBHzIuLxiNikrI22EXFzRMzMt5sjYs2yOptGxBN5Gx9GxP9FRJTVGRIRYyNiQf51v1p98pIkSar36lV4zo0D1i7ZNi0pOxU4CTge2BKYCjwcEWuU1LkVGAB8M98GADdXFkZEa+BhYErexgnAKcCJJXUGAcOBvwKb51/viIita+5pSpIkaWXTqK47UIVFKaXJ5QfzkeGfAb9LKf09P3YoWYA+GLg2IvqQBebtUkrP5HWOAZ6KiI1SSuOAQ4AWwKEppXnAqxHRGzgxIi5JKaX8Oo+llM7LL39eROycHx9aS89bkiRJ9Vx9HHleP5+WMT4ibo+I9fPj6wGdgYcqK+bh90lg2/zQIGA28HRJe/8F5pTVeSo/t9KDQBegR0mdh1jSgyVtSJIkaTVU38Lzc8BhZKPHR5GF5acjon3+GLLpFqWmlJR1Bqblo8cA5I+nltWpqg0K1OnMUkTE0RExKiJGTZs2bWnVJEmStBKrV9M2Ukr3l+5HxLPAu8ChwLN10qmCUkrXAdcBDBw4MFVTXZIkSSuh+jbyvISU0mzgNWBDoHIedKeyap1KyiYDHUpXzsgfdyyrU1UbFKjzpbnYkiRJWn3U6/AcEc2A3sAkYDxZeB1cVr49X8xxfgZoRTZnudIgoGVZne3zcysNBiYC75XUGcySBrPkXGpJkiStZupVeI6IiyJix4hYL18W7k6y4PuXfO7yZcBpEbF/RPQFhpHdIHgrQErpdeABspU3BuVLzl0LjMhX2iCvOxcYFhF9I2J/4BfAJSVzpS8HdomIX0RE74j4JbBzfn1JkiStpurVnGdgHeA2YC1gGtk8521SShPy8guA5sBVQFuyGwx3Tyl9WtLGwcCVZKtjAPwTOK6yMKU0MyIG522MAmYAFwOXlNR5OiK+C5wLnAO8AxyUUnquRp+tJEmSVir1KjynlL5bTXkCzsq3pdWZAXyvmnZeAXaops6dZCPfkiRJElDPpm1IkiRJ9ZnhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVVK/Cc0T8MiKej4hZETEtIu6NiL5ldYZFRCrbni2r0zQiroyIjyJiTkT8MyLWKavTLW9/Tl7viohoUlZnx4gYHRHzI+LdiDi29p69JEmS6rt6FZ6BnYCrgW2BXYBFwCMR0a6s3iPA2iXbXmXllwFDgKHA9kBrYERENATIv/4LWCMvHwocAFxc2UBErAfcBzwN9AfOB66MiCE18kwlSZK00mlU1x0olVLao3Q/Ir4PzAS+AdxbUrQgpTS5qjYiog3wQ+DwlNLDJe1MAHYDHgR2BzYBuqeUPsjrnAr8KSLOSCnNAo4FJqaUjs+bfj0itgZOBv5eE89XkiRJK5f6NvJcbg2yPs4oO75dREyNiDcj4vqI6FhStgXQGHio8kAekF8nG9EGGAS8Xhmccw8CTfPzK+s8xJIeBAZGROOv8ZwkSZK0kqrv4fly4CXgmZJjDwA/AHYFTgK2Ah6NiKZ5eWdgMfBRWVtT8rLKOlPKyj/Kz1tWnSlko/VrlXc0Io6OiFERMWratGlFnpskSZJWMvVq2kapiLgE2A7YLqW0uPJ4Sun2kmqvRMRosikZewN3rdhefiGldB1wHcDAgQNTXfVDkiRJtadejjxHxKVkN/HtklJ6d1l1U0oTgf8BG+aHJgMN+fLocKe8rLJOp7LytfLzllWnE9lNjOWj2pIkSVoN1LvwHBGX80VwfqNA/bWArsCk/NBoYCEwuKTOOkAfspUzIJsG0qds+brBwIL8/Mo6g1nSYGBUSmnh8jwnSZIkrRrqVXiOiKuAw4GDgRkR0TnfWuXlrSLioogYFBE9ImInslU4pgJ3A6SUZgI3ABdExG4R0R+4GXiZbIk7yG4EfA24KSL6R8RuwIXA9flKGwB/BLpGxGUR0ScijgQOAy6q5ZdBkiRJ9VS9Cs/Aj8lW2Pg32Uhy5XZyXr4Y2BS4B3gT+AswDhiUUvq0pJ2fkYXp4cB/gdnAvpVzp/OvewNz8/LhZMvPVV6HlNJ4svWjdyC7afEM4KcpJZepkyRJWk3VqxsGU0pRTfk8YI9l1cnrLQCOz7el1Xkf2Keadp4ABlR3PUmSJK0e6tvIsyRJklRvGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJUkOFZkiRJKsjwLEmSJBVkeJYkSZIKMjxLkiRJBRmeJUmSpIIMz5IkSVJBhmdJkiSpIMOzJEmSVJDhWZIkSSrI8CxJkiQVZHiWJEmSCjI8S5IkSQUZniVJkqSCDM+SJElSQYZnSZIkqSDDsyRJklSQ4VmSJEkqyPAsSZIkFWR4liRJkgoyPEuSJEkFGZ4lSZKkggzP1YiIH0fE+IiYHxGjI2L7uu6TJEmS6obheRki4iDgcuC3QH/gaeD+iOhWpx2TJElSnTA8L9uJwLCU0vUppddTSscDk4Af1XG/JEmSVAcMz0sREU2ALYCHyooeArZd8T2SJElSXTM8L91aQENgStnxKUDnFd8dSZIk1bVIKdV1H+qliOgCfAjsmFJ6suT4/wGHpJQ2Kqt/NHB0vrsRMG5F9VWSpHrgo5TSN+u6E1Jta1TXHajHPgIWA53KjncCJpdXTildB1y3AvolSZKkOuK0jaVIKX0GjAYGlxUNJlt1Q5IkSasZR56X7RLg5ogYCfwXOBboAvyxTnslSZKkOmF4XoaU0vCIaA/8ClgbeBXYK6U0oW57JqmIiDgLOCCl1Leu+yJJWjV4w6C0CouIYcBaKaV9luOcx4FXU0rH1Va/alpE9ADGA1umlEaVHG8FNE0pfVyL167uH9G/pJQOq63rS5JWLEeeJdWKiGiS3ztQZ1JKs4HZtXyZtUse7wNcX3ZsXi1fX5K0AnnDoLQaiYhhETEiIk6IiA8jYkZE/DkiWlSWAzsCP4mIlG898rKNI+JfEfFpREyNiNsionMVbZ8WEf8D/hcRv42I0VX04+mIuKJk//CIGBsR8yPizYj4eUQ0KClPEXF0RNwREXMi4t2I+F5Jk+Pzr8/ndR/PzzsrIl4taadBRPw6Ij6IiAUR8UpEfLukvEd+/pCIeDgi5ub9Kr9x+HMppcmVG/BJ5TGyNeH/AxxV9tw3zK8xoOS5HZe/tnMjYkLZcyMiukbE7fn3a0Zed8Ol9UmSVHsMz9LqZ3ugL7AbcBCwH3BCXnYC8AzwZ7LR07WBDyJibeBJsnn/W+XntgLuKQ25ZMF7M+CbwK7ALcCAiOhdWSEi1gcG5WVExFHAb4H/A/oAJwGnAT8u6/f/AfcA/YDhwI0R0S0v2yr/+s28z/sv5bmfAJySt78pcDdwV0RsXlbvPOCK/FrPA7fnU0AKS9mcuBuAw8uKjgBeSim9UHLsbOCfwOZkS17eFBEDAfI/bB4D5pO9voOAScAjlX/0SJJWHMOztPqZBRybUno9pfQQcAdZ0CWlNBP4DJhbMqK6GPgRMCaldFp+3svAD8hC68CStucDR6SUXk0pvZJSGgu8CBxSUudg4M2U0sh8/9fAqSmlO1NK41NK9wK/48vh+eaU0i0ppbfzcxYBO+Rl0/KvH+d9nr6U534ycFFK6daU0psppf8DnsqPl7o0pXRvSukt4HSgHVmwXV5/BnpFxDYAEdGQ7HW7oazeXSmla/M+nQc8CvwsL/suEMDhKaWXU0pvAMeQ/fFSeC67JKlmGJ6l1c/YPBBXmgh0rOacLYAdImJ25QZ8kJf1LKn3akppQdm5t5AF5kqHAH8FiIgOwLrAtWVt/66sXYCXKx+klBaRBebq+v25iGhNttTkf8uK/gNsvLRrkb0+LM+1KuXTN0aQjTZDNjLejvz5l3imiv3KPm0BrAd8WvL6zATa8uXXSJJUy7xhUFr9LCzbT1T/h3QD4F98eYQWsrm9leZUUX4bcEFEDAIWAL3Jp2yUXPdYqv/woa/S76LKV8z4/FoppRQRfI1r/Qm4NSJ+Rhai704pzViO8xsAL5GNQJdb2gi7JKmWGJ4llfsMaFh27AXgQGBCSqk8xC5TSmlSRDxKNuK8AHgmpfRuXjYlIiYCPVNKN33NPlNFv0v7MSu/1jeAf5cUbQeM/RrXrs4D5FNlgH2Bvaqosw1wY9n+6/njF4ChwEcppU9qr5uSpCIMz5LKvQdsla+yMZtsdPMqslUjhkfE78mmTKxPFqhPSil9Wk2btwAXk4Xc88rKzgSujIhPgPuAxsAAoGtK6fyCfZ5KtiTcHhHxHjA/n79d7kLgnIh4CxgNfI/sBsoBBa+z3FJKiyPiRuB84EOWDO6V9o+I54HHgQPI5qBvnZf9lWzE/56I+D/gfbKpLt8G/pjPy5YkrSDOeZZU7iKykDuWLCR3SylVjthWkI2kvkYWqBfkW3XuAloAHchWyvhcSulPZNMZvg+MIbuB72i+WH6uWvkc6J8CR5LNUb5nKVWvIAvQF5CtHLIfMCSlNKbotb6iG4EmwJ9T1Z9MdRYwhGyu9Y/Ibg58HiClNJfsxsh3yW7ufAP4C9mc5+WZ/iFJqgF+wqAk1bKI2JrsRsX1U0rvl5Ul4P+llO6sk85JkpaL0zYkqZZERFOy0fbfkN0o+H41p0iS6jmnbUhS7RkKTADWAk6s475IkmqA0zYkSZKkghx5liRJkgoyPEuSJEkFGZ4lSZKkggzPkiRJUkGGZ0mSJKkgw7MkSZJU0P8HanwRTTf33+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the average number of engagements by intervention type\n",
    "plt.figure(figsize=(8, 10))\n",
    "\n",
    "plt.bar(0, width=.5, height=hard_interactions, color=hard_intervention_color, label='Hard Intervention')\n",
    "plt.bar(1, width=.5, height=soft_interactions, color=soft_intervention_color, label='Soft Itervention')\n",
    "plt.bar(2, width=.5, height=none_interactions, color=no_intervention_color, label='No Intervention')\n",
    "plt.legend(fontsize=14)\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14, bbox_to_anchor = [1, .5])\n",
    "plt.xlabel('Intervention Type', fontsize=14)\n",
    "plt.ylabel('Average Engagement', fontsize=14)\n",
    "plt.title('Average Engagement by Intervention Type - Twitter', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/twitter_avg_engagement.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "promotional-latex",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fleet-scoop",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "israeli-lancaster",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "changing-despite",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "descending-sussex",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
